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Chenghu Jing^1,2, Hui Zhang³, Yafeng Liu²

¹Henan Key Laboratory of Superhard Abrasives and Grinding Equipment, Henan University of Technology, Zhengzhou 450001, China.

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Summary

This study introduces a modified adaptive super-twisting sliding mode control (ASTSMC) for robotic manipulators. The novel approach enhances robustness and ensures finite-time convergence, even with input saturation and unknown uncertainties.

Keywords:

finite time input saturation robot manipulators robust adaptive sliding mode super twisting

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Area of Science:

Robotics
Control Systems Engineering
Applied Mathematics

Background:

Robotic manipulators require robust control strategies to handle uncertainties and disturbances.
Input saturation is a common practical limitation affecting manipulator performance.
Existing sliding mode control methods may struggle with unknown uncertainty bounds and convergence rates.

Purpose of the Study:

To develop a modified adaptive super-twisting sliding mode control (ASTSMC) for robotic manipulators.
To address challenges posed by input saturation and unknown lumped uncertainties.
To ensure finite-time convergence and enhance robustness.

Main Methods:

A modified sliding mode surface (SMS) was designed to improve convergence speed and avoid singular perturbation.
A modified adaptive law was developed, eliminating the need for prior knowledge of disturbance boundaries.
An ASTSMC with saturation compensation was proposed to mitigate the impact of input saturation.

Main Results:

The proposed ASTSMC scheme demonstrated strong adaptability and effective control gain adjustment.
The method achieved finite-time convergence for robotic manipulators.
Simulations confirmed superior robustness against disturbances and uncertainties compared to other control schemes.

Conclusions:

The developed modified ASTSMC offers a robust and efficient solution for controlling robotic manipulators.
The approach effectively handles input saturation and unknown uncertainties, ensuring reliable performance.
The finite-time convergence analysis validates the stability and effectiveness of the proposed control strategy.